Beam Alignment and Tracking for Millimeter Wave Communications via Bandit Learning

Zhang, Jianjun; Huang, Yongming; Yu, Zhiping; You, Xiaohu

doi:10.1109/tcomm.2020.2988256

Cited by 64 publications

(39 citation statements)

References 28 publications

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“…learning [364]. The results show that they can capture dynamic spatial patterns and adjust beam training strategy intelligently, without knowing priori information about dynamic channel modeling.…”

Section: Physical Layer Applicationsmentioning

confidence: 99%

“…As an extension of model-driven AI and DL, an interactive learning design paradigm (ILDP) recently is proposed to make full use of domain knowledge of wireless communications and adaptive learning ability of AI and DL [363,364]. In contrast to the conventional model-driven approach that follows a non-interactive paradigm, the ILDP consists of communication model module and adaptive learning module, which work in an interactive manner, thus is able to extract useful information in real-time and sufficiently adapt to the ever-changing environments.…”

Section: Physical Layer Applicationsmentioning

confidence: 99%

“…In [363], DL was used to generate an end-to-end system, which includes all signal-related functionalities. In [364], a new algorithm was developed to design an end-to-end system even when the channel model is unknown. This functionality is achieved by iteratively training both the Tx and the Rx with true gradient and approximated gradient, respectively.…”

Section: Physical Layer Applicationsmentioning

confidence: 99%

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

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1,216

495

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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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Section: Physical Layer Applicationsmentioning

confidence: 99%

Section: Physical Layer Applicationsmentioning

confidence: 99%

Section: Physical Layer Applicationsmentioning

confidence: 99%

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Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

You

Wang

Huang

et al. 2020

Sci. China Inf. Sci.

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1,216

495

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“…where q(t) incorporates possible uncertainties caused by system or modeling error. To facilitate inference, a multi-output GP (MO-GP) is used to characterize the mapping in (19).…”

Section: A Coordinate Prediction Via Beamsmentioning

confidence: 99%

“…Since the BA algorithms proposed in this paper, i.e., Algorithm 1, Algorithm 2 and Algorithm 4, are all designed based on GP learning, they are abbreviated as GPL-1, GPL-2 and GPL-4, respectively. In addition to the conventional hierarchical search (HS) based BA algorithm [4], several ML based BA algorithms proposed recently, i.e., direct upper confidence bound (DUCB) [16], hierarchical posterior matching (HPM) [20], partially observable Markov decision process (POMDP) with some modifications [21] and stochastic bandit learning (SBL) [19], are adopted for comparison. EAR [19] and probability of successful alignment (PSA) are used as performance metrics to evaluate different algorithms.…”

Section: B Performance Of Beam Alignmentmentioning

confidence: 99%

Learning-Based Predictive Transmitter-Receiver Beam Alignment in Millimeter Wave Fixed Wireless Access Links

Zhang

Masouros

2021

IEEE Trans. Signal Process.

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Millimeter wave (mmwave) fixed wireless access is a key enabler of 5G and beyond small cell network deployment, exploiting the abundant mmwave spectrum to provide Gbps backhaul and access links. Large antenna arrays and extremely directional beamforming are necessary to combat the mmwave path loss. However, narrow beams increase sensitivity to physical perturbations caused by environmental factors. To address this issue, in this paper we propose a predictive transmit-receive beam alignment process. We construct an explicit mapping between transmit (or receive) beams and physical coordinates via a Gaussian process, which can incorporate environmental uncertainties. To make full use of underlying correlations between transmitter and receiver and accumulated experiences, we further construct a hierarchical Bayesian learning model and design an efficient beam predictive algorithm. To reduce dependency on physical position measurements, a reverse mapping that predicts physical coordinates from beam experiences is further constructed. The designed algorithms enjoy two folds of advantages. Firstly, thanks to Bayesian learning, a good performance can be achieved even for a small sample setting as low as 10 samples in our scenarios, which drastically reduces training time and is therefore very appealing for wireless communications. Secondly, in contrast to most existing algorithms that only predict one beam in each timeslot, the designed algorithms generate the most promising beam subset, which improves robustness to environment uncertainties. Simulation results demonstrate the effectiveness and superiority of the designed algorithms against the state of the art. Index Terms-Beam alignment, Bayesian learning, beam prediction, beam training, fixed wireless access, Gaussian processes, millimeter wave communications. I. INTRODUCTIONM ILLIMETER wave (mmwave) fixed wireless access (FWA), which is also referred to as mmwave distribution network and supported by the IEEE 802.11ay, enables different deployment scenarios, including broadband residential access, WiFi access point (AP), small cell backhaul, and home media sharing [1]. By operating in the mmwave bands, mmwave FWA provides much increased capacity compared to other WiFi systems that operate in the microwave bands. Moreover, since mmwave links are highly directional, it also presents significant opportunities for spatial reuse,

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Federated Learning‐Based Beam Management in Dense Millimeter Wave Communication Systems

Xue,

Yang

2023

Federated Learning for Future Intelligent Wireless Networks

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Beam Alignment and Tracking for Millimeter Wave Communications via Bandit Learning

Cited by 64 publications

References 28 publications

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Learning-Based Predictive Transmitter-Receiver Beam Alignment in Millimeter Wave Fixed Wireless Access Links

Federated Learning‐Based Beam Management in Dense Millimeter Wave Communication Systems

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